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Register a new userData Analytics Approach to Predict High-Temperature Cyclic Oxidation Kinetics of NiCr-based Alloys
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Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic-oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants (kp). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over a wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.
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A breakthrough in alloy design often requires comprehensive understanding in complex multi-component/multi-phase systems to generate novel material hypotheses. We introduce a modern data analytics workflow that leverages high-quality experimental data augmented with advanced features obtained from high-fidelity models. Herein, we use an example of a consistently-measured creep dataset of developmental high-temperature alloy combined with scientific alloy features populated from a high-throughput computational thermodynamic approach. Extensive correlation analyses provide ranking insights for most impactful alloy features for creep resistance, evaluated from a large set of candidate features suggested by domain experts. We also show that we can accurately train machine learning models by integrating high-ranking features obtained from correlation analyses. The demonstrated approach can be extended beyond incorporating thermodynamic features, with input from domain experts used to compile lists of features from other alloy physics, such as diffusion kinetics and microstructure evolution.
The microstructural features and high-temperature oxidation resistance of hybrid (TiC+TiB) networks reinforced Ti-6Al-4V composites were investigated after fabricated with reaction hot pressing technique. The inhomogeneous distribution of hybrid reinforcers resulted in a sort of stress-induced grain refinement for {alpha}-Ti matrix phase, which was further facilitated by heterogeneous nucleation upon additive interfaces. HRTEM analyses revealed the crystallographic orientation relation between TiB and alpha-Ti phases as (201)TiB//(-1100)alpha-Ti plus [11-2]//[0001] alpha-Ti, while TiC and {alpha}-Ti phases maintained the interrelation of (-200)TiC//(-2110) {alpha}-Ti and [001]TiC//[01-10] alpha-Ti. The hybridly reinforced Ti-6Al-4V/(TiC+TiB) composites displayed superior oxidation resistance to both the sintered matrix alloy and the two composites reinforced solely with TiC or TiB addition during the cyclic oxidation at 873, 973 and 1073 K respectively for 100 h. The hybrid reinforcers volume fraction was a more influential factor to improve oxidation resistance than the matrix alloy powder size. As temperature rose from 873 to 1073 K, the oxidation kinetics transferred from the nearly parabolic type through qusilinear tendency into the finally linear mode. This corresponded to the morphological transition of oxide scales from a continuous protective film to a partially damaged layer and ended up with the complete spallation of alternating alumina and rutile multilayers. A phenomenological model was proposed to elucidate the growth process of oxides scales. The release of thermal stress, the suppression of oxygen diffusion and the fastening of oxide adherence were found as the three major mechanisms to enhance the oxidation resistance of hybrid reinforced composites.
High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex high-temperature alloys with an example of the 9-12 wt.% Cr steels yield strength. We have incorporated synthetic alloy features that capture microstructure and phase transformations into the dataset. Identified high impact features that affect yield strength of 9Cr from correlation analysis agree well with the generally accepted strengthening mechanism. As part of the verification process, the consistency of sub-datasets has been extensively evaluated with respect to temperature and then refined for the boundary conditions of trained ML models. The predicted yield strength of 9Cr steels using the ML models is in excellent agreement with experiments. The current approach introduces physically meaningful constraints in interrogating the trained ML models to predict properties of hypothetical alloys when applied to data-driven materials.
Multi-principal-element alloys, including high-entropy alloys, experience segregation or partially-ordering as they are cooled to lower temperatures. For Ti$_{0.25}$CrFeNiAl$_{x}$, experiments suggest a partially-ordered B2 phase, whereas CALculation of PHAse Diagrams (CALPHAD) predicts a region of L2$_{1}$+B2 coexistence. We employ first-principles density-functional theory (DFT) based electronic-structure approach to assess stability of phases of alloys with arbitrary compositions and Bravais lattices (A1/A2/A3). In addition, DFT-based linear-response theory has been utilized to predict Warren-Cowley short-range order (SRO) in these alloys, which reveals potentially competing long-range ordered phases. The resulting SRO is uniquely analyzed using concentration-waves analysis for occupation probabilities in partially-ordered states, which is then be assessed for phase stability by direct DFT calculations. Our results are in good agreement with experiments and CALPHAD in Al-poor regions ($x le 0.75$) and with CALPHAD in Al-rich region ($0.75 le {x} le 1$), and they suggest more careful experiments in Al-rich region are needed. Our DFT-based electronic-structure and SRO predictions supported by concentration-wave analysis are shown to be a powerful method for fast assessment of competing phases and their stability in multi-principal-element alloys.
Vanadium dioxide (VO2) is a phase transition material that undergoes a reversible insulator-metal phase transition at ~ 68 C. Atmospheric pressure thermal oxidation (APTO) of vanadium (V) is a simple VO2 synthesis method in which V thin film is oxidized in open air. For an optimum oxidation duration, VO2 films are obtained with good phase transition properties. We recently reported a modified APTO process using a step temperature profile for oxidation (Thin Solid Films 706, 138003 (2020)). We demonstrated an ultra-low thermal budget synthesis of VO2 thin films with good electrical and optical phase transition properties. For a 130 nm room-temperature RF sputtered V thin film, an optimum oxidation duration of ~ 30 s was obtained. In this work, we study how the starting V film thickness and deposition temperature affects the optimum oxidation duration. V thin films of varying thickness (15-212 nm) and 120 nm thick V films with varying deposition temperature (~27-450 C) are prepared using RF magnetron sputtering. These films are oxidized for different oxidation durations and characterized using Raman and four-probe measurements to find the optimum oxidation duration for each deposition condition. We find that the optimum oxidation duration increases with the increase in V film thickness and V deposition temperature. We model the effect of V film thickness and deposition temperature on the optimal oxidation time using a parabolic law which can be used to obtain the optimal oxidation times for intermediate V thicknesses/deposition temperatures.
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